CovidStanfordV1, Main, Exploration, bibRecord, 000784

Automated EHR score to predict COVID-19 outcomes at US Department of Veterans Affairs.

Identifieur interne : 000784 ( Main/Exploration ); précédent : 000783; suivant : 000785

Automated EHR score to predict COVID-19 outcomes at US Department of Veterans Affairs.

Auteurs : Thomas F. Osborne [États-Unis] ; Zachary P. Veigulis [États-Unis] ; David M. Arreola [États-Unis] ; Eliane Röösli [États-Unis] ; Catherine M. Curtin [États-Unis]

Source :

PloS one [ 1932-6203 ] ; 2020.

RBID : pubmed:32716922

Descripteurs français

KwdFr :
- Adulte d'âge moyen (MeSH), Appréciation des risques (méthodes), Department of Veterans Affairs (USA) (MeSH), Dossiers médicaux électroniques (MeSH), Femelle (MeSH), Humains (MeSH), Hôpitaux des anciens combattants (MeSH), Infections à coronavirus (diagnostic), Infections à coronavirus (mortalité), Infections à coronavirus (thérapie), Modèles logistiques (MeSH), Mâle (MeSH), Pandémies (MeSH), Pneumopathie virale (diagnostic), Pneumopathie virale (mortalité), Pneumopathie virale (thérapie), Prise de décision (MeSH), Résultat thérapeutique (MeSH), Voies des issues indésirables (MeSH), États-Unis (MeSH), Études rétrospectives (MeSH), Évaluation des besoins (MeSH).
MESH :
- diagnostic : Infections à coronavirus, Pneumopathie virale.
- mortalité : Infections à coronavirus, Pneumopathie virale.
- méthodes : Appréciation des risques.
- thérapie : Infections à coronavirus, Pneumopathie virale.
- Adulte d'âge moyen, Department of Veterans Affairs (USA), Dossiers médicaux électroniques, Femelle, Humains, Hôpitaux des anciens combattants, Modèles logistiques, Mâle, Pandémies, Prise de décision, Résultat thérapeutique, Voies des issues indésirables, États-Unis, Études rétrospectives, Évaluation des besoins.
Wicri :
- geographic : États-Unis.

English descriptors

KwdEn :
- Adverse Outcome Pathways (MeSH), COVID-19 (MeSH), Coronavirus Infections (diagnosis), Coronavirus Infections (mortality), Coronavirus Infections (therapy), Decision Making (MeSH), Electronic Health Records (MeSH), Female (MeSH), Hospitals, Veterans (MeSH), Humans (MeSH), Logistic Models (MeSH), Male (MeSH), Middle Aged (MeSH), Needs Assessment (MeSH), Pandemics (MeSH), Pneumonia, Viral (diagnosis), Pneumonia, Viral (mortality), Pneumonia, Viral (therapy), Retrospective Studies (MeSH), Risk Assessment (methods), Treatment Outcome (MeSH), United States (MeSH), United States Department of Veterans Affairs (MeSH).
MESH :
- geographic : United States.
- diagnosis : Coronavirus Infections, Pneumonia, Viral.
- methods : Risk Assessment.
- mortality : Coronavirus Infections, Pneumonia, Viral.
- therapy : Coronavirus Infections, Pneumonia, Viral.
- Adverse Outcome Pathways, COVID-19, Decision Making, Electronic Health Records, Female, Hospitals, Veterans, Humans, Logistic Models, Male, Middle Aged, Needs Assessment, Pandemics, Retrospective Studies, Treatment Outcome, United States Department of Veterans Affairs.

Abstract

The sudden emergence of COVID-19 has brought significant challenges to the care of Veterans. An improved ability to predict a patient's clinical course would facilitate optimal care decisions, resource allocation, family counseling, and strategies for safely easing distancing restrictions. The Care Assessment Need (CAN) score is an existing risk assessment tool within the Veterans Health Administration (VA), and produces a score from 0 to 99, with a higher score correlating to a greater risk. The model was originally designed for the nonacute outpatient setting and is automatically calculated from structured data variables in the electronic health record. This multisite retrospective study of 6591 Veterans diagnosed with COVID-19 from March 2, 2020 to May 26, 2020 was designed to assess the utility of repurposing the CAN score as objective and automated risk assessment tool to promptly enhance clinical decision making for Veterans diagnosed with COVID-19. We performed bivariate analyses on the dichotomized CAN 1-year mortality score (high vs. low risk) and each patient outcome using Chi-square tests of independence. Logistic regression models using the continuous CAN score were fit to assess its predictive power for outcomes of interest. Results demonstrated that a CAN score greater than 50 was significantly associated with the following outcomes after positive COVID-19 test: hospital admission (OR 4.6), prolonged hospital stay (OR 4.5), ICU admission (3.1), prolonged ICU stay (OR 2.9), mechanical ventilation (OR 2.6), and mortality (OR 7.2). Repurposing the CAN score offers an efficient way to risk-stratify COVID-19 Veterans. As a result of the compelling statistical results, and automation, this tool is well positioned for broad use across the VA to enhance clinical decision-making.

DOI: 10.1371/journal.pone.0236554
PubMed: 32716922
PubMed Central: PMC7384633

Affiliations:

Links toward previous steps (curation, corpus...)

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Le document en format XML

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<term>Coronavirus Infections (mortality)</term>
<term>Coronavirus Infections (therapy)</term>
<term>Decision Making (MeSH)</term>
<term>Electronic Health Records (MeSH)</term>
<term>Female (MeSH)</term>
<term>Hospitals, Veterans (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Logistic Models (MeSH)</term>
<term>Male (MeSH)</term>
<term>Middle Aged (MeSH)</term>
<term>Needs Assessment (MeSH)</term>
<term>Pandemics (MeSH)</term>
<term>Pneumonia, Viral (diagnosis)</term>
<term>Pneumonia, Viral (mortality)</term>
<term>Pneumonia, Viral (therapy)</term>
<term>Retrospective Studies (MeSH)</term>
<term>Risk Assessment (methods)</term>
<term>Treatment Outcome (MeSH)</term>
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<term>United States Department of Veterans Affairs (MeSH)</term>
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<term>Appréciation des risques (méthodes)</term>
<term>Department of Veterans Affairs (USA) (MeSH)</term>
<term>Dossiers médicaux électroniques (MeSH)</term>
<term>Femelle (MeSH)</term>
<term>Humains (MeSH)</term>
<term>Hôpitaux des anciens combattants (MeSH)</term>
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<term>Infections à coronavirus (mortalité)</term>
<term>Infections à coronavirus (thérapie)</term>
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<term>Pneumopathie virale (mortalité)</term>
<term>Pneumopathie virale (thérapie)</term>
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<front><div type="abstract" xml:lang="en">The sudden emergence of COVID-19 has brought significant challenges to the care of Veterans. An improved ability to predict a patient's clinical course would facilitate optimal care decisions, resource allocation, family counseling, and strategies for safely easing distancing restrictions. The Care Assessment Need (CAN) score is an existing risk assessment tool within the Veterans Health Administration (VA), and produces a score from 0 to 99, with a higher score correlating to a greater risk. The model was originally designed for the nonacute outpatient setting and is automatically calculated from structured data variables in the electronic health record. This multisite retrospective study of 6591 Veterans diagnosed with COVID-19 from March 2, 2020 to May 26, 2020 was designed to assess the utility of repurposing the CAN score as objective and automated risk assessment tool to promptly enhance clinical decision making for Veterans diagnosed with COVID-19. We performed bivariate analyses on the dichotomized CAN 1-year mortality score (high vs. low risk) and each patient outcome using Chi-square tests of independence. Logistic regression models using the continuous CAN score were fit to assess its predictive power for outcomes of interest. Results demonstrated that a CAN score greater than 50 was significantly associated with the following outcomes after positive COVID-19 test: hospital admission (OR 4.6), prolonged hospital stay (OR 4.5), ICU admission (3.1), prolonged ICU stay (OR 2.9), mechanical ventilation (OR 2.6), and mortality (OR 7.2). Repurposing the CAN score offers an efficient way to risk-stratify COVID-19 Veterans. As a result of the compelling statistical results, and automation, this tool is well positioned for broad use across the VA to enhance clinical decision-making.</div>
</front>
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<Abstract><AbstractText>The sudden emergence of COVID-19 has brought significant challenges to the care of Veterans. An improved ability to predict a patient's clinical course would facilitate optimal care decisions, resource allocation, family counseling, and strategies for safely easing distancing restrictions. The Care Assessment Need (CAN) score is an existing risk assessment tool within the Veterans Health Administration (VA), and produces a score from 0 to 99, with a higher score correlating to a greater risk. The model was originally designed for the nonacute outpatient setting and is automatically calculated from structured data variables in the electronic health record. This multisite retrospective study of 6591 Veterans diagnosed with COVID-19 from March 2, 2020 to May 26, 2020 was designed to assess the utility of repurposing the CAN score as objective and automated risk assessment tool to promptly enhance clinical decision making for Veterans diagnosed with COVID-19. We performed bivariate analyses on the dichotomized CAN 1-year mortality score (high vs. low risk) and each patient outcome using Chi-square tests of independence. Logistic regression models using the continuous CAN score were fit to assess its predictive power for outcomes of interest. Results demonstrated that a CAN score greater than 50 was significantly associated with the following outcomes after positive COVID-19 test: hospital admission (OR 4.6), prolonged hospital stay (OR 4.5), ICU admission (3.1), prolonged ICU stay (OR 2.9), mechanical ventilation (OR 2.6), and mortality (OR 7.2). Repurposing the CAN score offers an efficient way to risk-stratify COVID-19 Veterans. As a result of the compelling statistical results, and automation, this tool is well positioned for broad use across the VA to enhance clinical decision-making.</AbstractText>
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<AffiliationInfo><Affiliation>US Department of Veterans Affairs, Palo Alto Healthcare System, Palo Alto, California, United States of America.</Affiliation>
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<AffiliationInfo><Affiliation>Department of Radiology, Stanford University School of Medicine, Stanford, California, United States of America.</Affiliation>
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<Author ValidYN="Y"><LastName>Veigulis</LastName>
<ForeName>Zachary P</ForeName>
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<AffiliationInfo><Affiliation>US Department of Veterans Affairs, Central Iowa Health Care System, Des Moines, Iowa, United States of America.</Affiliation>
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<Author ValidYN="Y"><LastName>Arreola</LastName>
<ForeName>David M</ForeName>
<Initials>DM</Initials>
<AffiliationInfo><Affiliation>US Department of Veterans Affairs, Palo Alto Healthcare System, Palo Alto, California, United States of America.</Affiliation>
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<ForeName>Eliane</ForeName>
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<Author ValidYN="Y"><LastName>Curtin</LastName>
<ForeName>Catherine M</ForeName>
<Initials>CM</Initials>
<Identifier Source="ORCID">0000-0003-0850-9277</Identifier>
<AffiliationInfo><Affiliation>US Department of Veterans Affairs, Palo Alto Healthcare System, Palo Alto, California, United States of America.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Surgery, Stanford University School of Medicine, Stanford, California, United States of America.</Affiliation>
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<MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName>
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<QualifierName UI="Q000401" MajorTopicYN="N">mortality</QualifierName>
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Serveur d'exploration sur le Covid à Stanford

Automated EHR score to predict COVID-19 outcomes at US Department of Veterans Affairs.

Automated EHR score to predict COVID-19 outcomes at US Department of Veterans Affairs.

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